SoroushVahidi/scidocs huggingface dataset:
CORONET: Fault tolerance for Software Defined Networks
(2012)

Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.
Alteration of the platelet serotonin transporter in romantic love.
(1999)

BACKGROUND
The evolutionary consequences of love are so important that there must be some long-established biological process regulating it. Recent findings suggest that the serotonin (5-HT) transporter might be linked to both neuroticism and sexual behaviour as well as to obsessive-compulsive disorder (OCD). The similarities between an overvalued idea, such as that typical of subjects in the early phase of a love relationship, and obsession, prompted us to explore the possibility that the two conditions might share alterations at the level of the 5-HT transporter.


METHODS
Twenty subjects who had recently (within the previous 6 months) fallen in love, 20 unmedicated OCD patients and 20 normal controls, were included in the study. The 5-HT transporter was evaluated with the specific binding of 3H-paroxetine (3H-Par) to platelet membranes.


RESULTS
The results showed that the density of 3H-Par binding sites was significantly lower in subjects who had recently fallen in love and in OCD patients than in controls.


DISCUSSION
The main finding of the present study is that subjects who were in the early romantic phase of a love relationship were not different from OCD patients in terms of the density of the platelet 5-HT transporter, which proved to be significantly lower than in the normal controls. This would suggest common neurochemical changes involving the 5-HT system, linked to psychological dimensions shared by the two conditions, perhaps at an ideational level.
Message authentication in driverless cars
(2016)

Driverless cars and driver-assisted vehicles are becoming prevalent in today's transportation systems. Companies like TESLA, Audi, and Google are among the leaders in this new era of the automotive industry. Modern vehicles, equipped with sophisticated navigation devices, complex driver-assisted systems, and a whole lot of safety features, bring broader impacts to our quality of life, economic development, and environmental sustainability. Instant safety messages such as pre-collision warnings, blind-spot detection, pedestrian and object awareness significantly improve the safety for drivers, passengers, and pedestrians. As a result, vehicles would be able to travel closely yet safely together, forming a platoon, thus resulting in a reduction of traffic congestion and fuel consumption. Driverless cars also have non-safety-related applications: which are used to facilitate traffic management and infotainment dissemination for drivers and passengers. Vehicular Ad hoc Network (VANET), the wireless communication technology enabling driverless cars, features not only dynamic topology but also high mobility. The vehicle nodes move rapidly on streets and highways. Their movements also depend on but not limited to road traffic, speed limits, and behavior of nearby vehicles. With massive amount of messages exchanged among driverless cars that command the cars' movements at high speeds and in close distances, any malicious alternation could lead to disastrous accidents. Message authentication to ensure data integrity is paramount to attack preparation. This paper presents a novel message authentication scheme that protects cars from bogus messages and makes VANET resilient to Denial-of-Service (DoS) attacks. The work includes a simulation framework that integrates vehicle and data traffic models to validate the effectiveness of the proposed message authentication scheme.
An overview of reservoir computing: theory, applications and implementations
(2007)

Training recurrent neural networks is hard. Recently it has however been discovered that it is possible to just construct a random recurrent topology, and only train a single linear readout layer. State-ofthe-art performance can easily be achieved with this setup, called Reservoir Computing. The idea can even be broadened by stating that any high dimensional, driven dynamic system, operated in the correct dynamic regime can be used as a temporal ‘kernel’ which makes it possible to solve complex tasks using just linear post-processing techniques. This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
(2002)

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
Adaptive Nonlinear System Identification with Echo State Networks
(2002)

Echo state networks (ESN) are a novel approach to recurrent neural network training. An ESN consists of a large, fixed, recurrent "reservoir" network, from which the desired output is obtained by training suitable output connection weights. Determination of optimal output weights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes an online adaptation scheme based on the RLS algorithm known from adaptive linear systems. As an example, a 10-th order NARMA system is adaptively identified. The known benefits of the RLS algorithms carryover from linear systems to nonlinear ones; specifically, the convergence rate and misadjustment can be determined at design time.
Spine dynamics as a computational resource in spine-driven quadruped locomotion
(2013)

Recent results suggest that compliance and non-linearity in physical bodies of soft robots may not be disadvantageous properties with respect to control, but rather of advantage. In the context of morphological computation one could see such complex structures as potential computational resources. In this study, we implement and exploit this view point in a spine-driven quadruped robot called Kitty by using its flexible spine as a computational resource. The spine is an actuated multi-joint structure consisting of a sequence of soft silicone blocks. Its complex dynamics are captured by a set of force sensors and used to construct a closed-loop to drive the motor commands. We use simple static, linear readout weights to combine the sensor values to generate multiple gait patterns (bounding, trotting, turning behavior). In addition, we demonstrate the robustness of the setup by applying strong external perturbations in form of additional loads. The system is able to fully recover to its nominal gait patterns (which are encoded in the linear readout weights) after the perturbation has vanished.
Variable impedance actuators: A review
(2013)

Variable Impedance Actuators (VIA) have received increasing attention in recent years as many novel applications involving interactions with an unknown and dynamic environment including humans require actuators with dynamics that are not well-achieved by classical stiff actuators. This paper presents an overview of the different VIAs developed and proposes a classification based on the principles through which the variable stiffness and damping are achieved. The main classes are active impedance by control, inherent compliance and damping actuators, inertial actuators, and combinations of them, which are then further divided into subclasses. This classification allows for designers of new devices to orientate and take inspiration and users of VIA’s to be guided in the design and implementation process for their targeted application.
The politics of comments: predicting political orientation of news stories with commenters' sentiment patterns
(2011)

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.
A machine learning approach for filtering Monte Carlo noise
(2015)

The most successful approaches for filtering Monte Carlo noise use feature-based filters (e.g., cross-bilateral and cross non-local means filters) that exploit additional scene features such as world positions and shading normals. However, their main challenge is finding the optimal weights for each feature in the filter to reduce noise but preserve scene detail. In this paper, we observe there is a complex relationship between the noisy scene data and the ideal filter parameters, and propose to learn this relationship using a nonlinear regression model. To do this, we use a multilayer perceptron neural network and combine it with a matching filter during both training and testing. To use our framework, we first train it in an offline process on a set of noisy images of scenes with a variety of distributed effects. Then at run-time, the trained network can be used to drive the filter parameters for new scenes to produce filtered images that approximate the ground truth. We demonstrate that our trained network can generate filtered images in only a few seconds that are superior to previous approaches on a wide range of distributed effects such as depth of field, motion blur, area lighting, glossy reflections, and global illumination.
Connecting Intelligent Things in Smart Hospitals Using NB-IoT
(2018)

The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.
MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds
(2011)

Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of decision criteria inputs, unlike human decision makers. It can be implemented as a web-based DSS to readily support cloud decision making world-wide, and improved further using fuzzy TOPSIS methods, to address concerns about preferential inter-dependence of attributes, insufficient input data or judgment expertise.
BRUTE: A High Performance and Extensibile Traffic Generator
(2005)

Evaluating the performance of high speed networks is a critical task due to the lack of reliable tools to generate traffic workloads at high rates and to the inaccessibility of network equipments. To address the problem of reliable device testing, we developed an extensible traffic generator, called BRUTE (Browny and RobUst Traffic Engine), running on top of Linux operating system. BRUTE takes advantage of the Linux kernel potential in order to accurately generate traffic flows up to very high bitrates. This software is an extensible framework that provides a number of optimized functions that easily allow the implementation of ad hoc modules for customized traffic patterns generation. A number of library modules implementing common traffic profiles (like CBR, Poisson process and Poissonian Arrival of Burst process) are already implemented and integrated into the package. Performance of BRUTE has been compared to the one of several widespread traffic generators over Fast-Ethernet and Gigabit-Ethernet networks. The results of our tests show the superior performance of BRUTE in terms of both the achieved throughput and the level of bitrate precision, under all possible values of the frame length.
Nuclei segmentation for computer-aided diagnosis of breast cancer
(2014)

Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.
Environmental sound classification with convolutional neural networks
(2015)

This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban recordings. The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.
Normally-off AlGaN/GaN HFET with p-type GaN gate and AlGaN buffer
(2010)

A 1.5 A normally-off GaN transistor for power applications in p-type GaN gate technology with a modified epitaxial concept is presented. A higher threshold voltage is achieved while keeping the on-state resistance low by using an Al-GaN buffer instead of a GaN buffer. Additionally, the AlGaN buffer acts as a back-barrier and suppresses source-drain punch-through currents in the off-state. P-GaN gate GaN transistors with AlGaN buffer will therefore yield higher breakdown voltages as compared to standard GaN buffer versions which results in an excellent VBr-to-RON ratio. The proposed normally-off technology shows save operation under elevated ambient temperature up to 200 °C without thermal runaway. In contrast to standard normally-on AlGaN/GaN HEMTs, a reverse diode operation is possible for offstate conditions which may enable improved inverter circuits.
Directed Multi-Objective Optimization
(2005)

While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framework for such problems by geometrically analyzing the multiobjective concepts of descent, diversity and convergence/optimality. It is shown that locally optimal, multi-objective descent direction can be calculated that maximally reduce all the objectives and a local sub-space also exists that is a basis for diversity updates. Local convergence of a point towards the optimal Pareto set is therefore assured. The concept of a population of points is also considered and it is shown that it can be used to locally increase the diversity of the population while still ensuring convergence and a method to extract the local directional information from the population is also described. The paper describes and introduces the basic theoretical concepts as well as demonstrating how they are used on simple test problems.
The Usable Privacy Policy Project : Combining Crowdsourcing , Machine Learning and Natural Language Processing to Semi-Automatically Answer Those Privacy Questions Users Care About
(2014)

Natural language privacy policies have become a de facto standard to address expectations of “notice and choice” on the Web. However, users generally do not read these policies and those who do read them struggle to understand their content. Initiatives aimed at addressing this problem through the development of machine-readable standards have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do. This project builds on recent advances in natural language processing, privacy preference modeling, crowdsourcing, formal methods, and privacy interface design to develop a practical framework based on websites’ existing natural language privacy policy that empowers users to more meaningfully control their privacy, without requiring additional cooperation from website operators. Our approach combines fundamental research with the development of scalable technologies to (1) semi-automatically extract key privacy policy features from natural language privacy policies, and (2) present these features to users in an easy-to-digest format that enables them to make more informed privacy decisions as they interact with different websites. This work will also involve the systematic collection and analysis of website privacy policies, looking for trends and deficiencies both in the wording and content of these policies across different sectors and using this analysis to inform public policy. This report outlines the project’s research agenda and overall approach.